Air conditioners enable a comfortable environment for people in a variety of scenarios. However, in the case of a room with multiple people, the specific comfort for a particular person is highly dependent on their clothes, metabolism, preference, and so on, and the ideal conditions for each person in a room can conflict with each other. An ideal way to resolve these kinds of conflicts is an intelligent air conditioning system that can independently control air temperature and flow at different areas in a room and then produce thermal comfort for multiple users, which we define as the personal preference of air flow and temperature. In this paper, we propose Personal Atmosphere, a machine learning based method to obtain parameters of air conditioners which generate non-uniform distributions of air temperature and flow in a room. In this method, two dimensional air-temperature and -flow distributions in a room are used as input to a machine learning model. These inputs can be considered a summary of each user’s preference. Then the model outputs a parameter set for air conditioners in a given room. We utilized ResNet-50 as the model and generated a data set of air temperature and flow distributions using computational fluid dynamics (CFD) software. We then conducted evaluations with two rooms that have two and four air conditioners under the ceiling. We then confirmed that the estimated parameters of the air conditioners can generate air temperature and flow distributions close to those required in simulation. We also evaluated the performance of a ResNet-50 with fine tuning. This result shows that its learning time is significantly decreased, but performance is also decreased.
Efficiency Enhancement on Thermal Comfort Assessment of Indoor Space with Air-Conditioner Using Computational Analysis